Alamri, E.S.; Altarawneh, G.A.; Bayomy, H.M.; Hassanat, A.B. Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants. Sustainability2023, 15, 11561.
Alamri, E.S.; Altarawneh, G.A.; Bayomy, H.M.; Hassanat, A.B. Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants. Sustainability 2023, 15, 11561.
Alamri, E.S.; Altarawneh, G.A.; Bayomy, H.M.; Hassanat, A.B. Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants. Sustainability2023, 15, 11561.
Alamri, E.S.; Altarawneh, G.A.; Bayomy, H.M.; Hassanat, A.B. Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants. Sustainability 2023, 15, 11561.
Abstract
This research assesses the classification of Arabic coffee into three primary variations (light, medium, and dark) using simulated data based on actual measurements of color information, antioxidant laboratory tests, and chemical composition tests. Two types of simulated data were generated, with the standard deviation of the measures varied. Multiple classifiers were used for classification. The findings demonstrate the use of color information in accurately distinguishing Arabic coffee types. The different CIE color values, as well as the excellent connection between color information and coffee classes, lead to flawless classification results. However, depending primarily on antioxidant information results in lower classification performance, with variability amongst classifiers due to increasing data complexity. Chemical composition information, on the other hand, possesses outstanding discriminatory strength, allowing faultless classification on its own. Certain characteristics, such as crude protein and crude fiber, show strong correlations and are critical in the classification of coffee types. Based on the findings, it is recommended to create a mobile application that uses image recognition to analyze coffee color while providing chemical composition information. End users, such as consumers, will be able to make informed judgments regarding their coffee preferences owing to this application.
Keywords
smart sustainability; smart food; Arabic coffee; classification; color information; antioxi-dant information; chemical composition; mobile application
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
